In recent years, photography has been rapidly transformed from chemical based technologies to digital imaging technologies. Images captured by digital cameras can be stored in computers and viewed on display devices. Users can also produce image prints based on the digital images. Such image prints can be generated locally using output devices such an inkjet printer or a dye sublimation printer or remotely by a photo printing service provider. Other products that can be produced using the digital images can include photo books, photo calendars, photo mug, photo T-shirt, and so on. A photo book can include a cover page and a plurality of image pages each containing one or more images. Designing a photobook can include many iterative steps such as selecting suitable images, selecting layout, selecting images for each page, selecting backgrounds, picture frames, overall Style, add text, choose text font, and rearrange the pages, images and text, which can be quite time consuming. It is desirable to provide methods to allow users to design and produce image albums in a time efficient manner.
Many digital images contain people's faces; creating high-quality image products naturally requires proper consideration of people faces. For example the most important and relevant people such as family members should have their faces be shown in image products while strangers' faces should be minimized. In another example, while pictures of different faces at a same scene can be included in an image-based product, the pictures of a same person at a same scene should normally be filtered to allow the best one(s) to be presented in the image product.
Faces need to be detected and group based on persons' identities before they can be properly selected and placed in image products. Most conventional face detection techniques concentrate on face recognition, assuming that a region of an image containing a single face has already been detected and extracted and will be provided as an input. Common face detection methods include: knowledge-based methods; feature-invariant approaches, including the identification of facial features, texture and skin color; template matching methods, both fixed and deformable; and appearance based methods. After faces are detected, face images of each individual can be categorized into a group regardless whether the identity of the individual is known or not. For example, if two individuals Person A and Person B are detected in ten images. Each of the images can be categorized or tagged one of the four types: A only; B only, A and B; or neither A nor B. Algorithmically, the tagging of face images require training based one face images of known persons (or face models), for example, the face images of family members or friends of a user who uploaded the images.
To save users' time, technologies have been developed by Shutterfly, Inc. and others to automatically create image products using users' images. These automatic methods are facing increasing challenges as people take more and more digital photos. A person or a family can easily take thousands of photos in an average vacation trip. A user often has hundreds of thousands to even millions of photos in his or her account. Automatic sorting, analyzing, grouping, and laying out such a great number photos in the correct and meaningful manner are an immense task.
There is still a need for more accurate methods to accurately group face images for different persons and incorporate the face images in image products.